Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3204
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory459.6 B

Variable types

Text2
Categorical2
Numeric18
DateTime1

Alerts

KPI_score is highly overall correlated with stockout_count_last_monthHigh correlation
stockout_count_last_month is highly overall correlated with KPI_scoreHigh correlation
item_id has unique valuesUnique
stockout_count_last_month has 294 (9.2%) zerosZeros

Reproduction

Analysis started2025-11-12 20:33:15.367210
Analysis finished2025-11-12 20:33:29.645790
Duration14.28 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

item_id
Text

Unique 

Distinct3204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.5 KiB
2025-11-12T15:33:29.756692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters25632
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3204 ?
Unique (%)100.0%

Sample

1st rowITM10000
2nd rowITM10001
3rd rowITM10002
4th rowITM10003
5th rowITM10004
ValueCountFrequency (%)
itm100001
 
< 0.1%
itm100111
 
< 0.1%
itm100231
 
< 0.1%
itm100021
 
< 0.1%
itm100031
 
< 0.1%
itm100041
 
< 0.1%
itm100051
 
< 0.1%
itm100061
 
< 0.1%
itm100071
 
< 0.1%
itm100081
 
< 0.1%
Other values (3194)3194
99.7%
2025-11-12T15:33:29.925081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15245
20.5%
I3204
12.5%
T3204
12.5%
M3204
12.5%
02045
 
8.0%
21945
 
7.6%
31145
 
4.5%
4940
 
3.7%
5940
 
3.7%
6940
 
3.7%
Other values (3)2820
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)25632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15245
20.5%
I3204
12.5%
T3204
12.5%
M3204
12.5%
02045
 
8.0%
21945
 
7.6%
31145
 
4.5%
4940
 
3.7%
5940
 
3.7%
6940
 
3.7%
Other values (3)2820
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15245
20.5%
I3204
12.5%
T3204
12.5%
M3204
12.5%
02045
 
8.0%
21945
 
7.6%
31145
 
4.5%
4940
 
3.7%
5940
 
3.7%
6940
 
3.7%
Other values (3)2820
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15245
20.5%
I3204
12.5%
T3204
12.5%
M3204
12.5%
02045
 
8.0%
21945
 
7.6%
31145
 
4.5%
4940
 
3.7%
5940
 
3.7%
6940
 
3.7%
Other values (3)2820
11.0%

category
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size205.4 KiB
Pharma
660 
Automotive
658 
Electronics
651 
Groceries
618 
Apparel
617 

Length

Max length11
Median length9
Mean length8.6086142
Min length6

Characters and Unicode

Total characters27582
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPharma
2nd rowAutomotive
3rd rowGroceries
4th rowAutomotive
5th rowAutomotive

Common Values

ValueCountFrequency (%)
Pharma660
20.6%
Automotive658
20.5%
Electronics651
20.3%
Groceries618
19.3%
Apparel617
19.3%

Length

2025-11-12T15:33:29.979035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-12T15:33:30.029851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pharma660
20.6%
automotive658
20.5%
electronics651
20.3%
groceries618
19.3%
apparel617
19.3%

Most occurring characters

ValueCountFrequency (%)
r3164
11.5%
e3162
11.5%
o2585
 
9.4%
t1967
 
7.1%
a1937
 
7.0%
i1927
 
7.0%
c1920
 
7.0%
m1318
 
4.8%
A1275
 
4.6%
s1269
 
4.6%
Other values (9)7058
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)27582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r3164
11.5%
e3162
11.5%
o2585
 
9.4%
t1967
 
7.1%
a1937
 
7.0%
i1927
 
7.0%
c1920
 
7.0%
m1318
 
4.8%
A1275
 
4.6%
s1269
 
4.6%
Other values (9)7058
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r3164
11.5%
e3162
11.5%
o2585
 
9.4%
t1967
 
7.1%
a1937
 
7.0%
i1927
 
7.0%
c1920
 
7.0%
m1318
 
4.8%
A1275
 
4.6%
s1269
 
4.6%
Other values (9)7058
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r3164
11.5%
e3162
11.5%
o2585
 
9.4%
t1967
 
7.1%
a1937
 
7.0%
i1927
 
7.0%
c1920
 
7.0%
m1318
 
4.8%
A1275
 
4.6%
s1269
 
4.6%
Other values (9)7058
25.6%

stock_level
Real number (ℝ)

Distinct479
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.49157
Minimum20
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.084337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile44
Q1144
median264
Q3386
95-th percentile475
Maximum499
Range479
Interquartile range (IQR)242

Descriptive statistics

Standard deviation138.56836
Coefficient of variation (CV)0.52589295
Kurtosis-1.2142356
Mean263.49157
Median Absolute Deviation (MAD)121
Skewness-0.030070597
Sum844227
Variance19201.19
MonotonicityNot monotonic
2025-11-12T15:33:30.142250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49015
 
0.5%
20614
 
0.4%
7814
 
0.4%
40914
 
0.4%
33214
 
0.4%
20714
 
0.4%
30213
 
0.4%
11513
 
0.4%
35613
 
0.4%
23313
 
0.4%
Other values (469)3067
95.7%
ValueCountFrequency (%)
203
 
0.1%
215
0.2%
228
0.2%
235
0.2%
247
0.2%
259
0.3%
265
0.2%
278
0.2%
282
 
0.1%
295
0.2%
ValueCountFrequency (%)
49912
0.4%
4988
0.2%
4975
 
0.2%
4964
 
0.1%
4959
0.3%
4945
 
0.2%
4938
0.2%
4923
 
0.1%
4915
 
0.2%
49015
0.5%

reorder_point
Real number (ℝ)

Distinct90
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.759363
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.199280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q132
median55
Q378
95-th percentile95
Maximum99
Range89
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.053354
Coefficient of variation (CV)0.47577897
Kurtosis-1.2074164
Mean54.759363
Median Absolute Deviation (MAD)23
Skewness0.00066668075
Sum175449
Variance678.77723
MonotonicityNot monotonic
2025-11-12T15:33:30.253307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2750
 
1.6%
9147
 
1.5%
8046
 
1.4%
3546
 
1.4%
6746
 
1.4%
5845
 
1.4%
7845
 
1.4%
1144
 
1.4%
8944
 
1.4%
3444
 
1.4%
Other values (80)2747
85.7%
ValueCountFrequency (%)
1033
1.0%
1144
1.4%
1233
1.0%
1334
1.1%
1425
0.8%
1535
1.1%
1636
1.1%
1728
0.9%
1841
1.3%
1935
1.1%
ValueCountFrequency (%)
9940
1.2%
9831
1.0%
9742
1.3%
9644
1.4%
9532
1.0%
9435
1.1%
9331
1.0%
9231
1.0%
9147
1.5%
9030
0.9%

reorder_frequency_days
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5078027
Minimum3
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.298641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median9
Q312
95-th percentile14
Maximum14
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4732286
Coefficient of variation (CV)0.40824038
Kurtosis-1.2413814
Mean8.5078027
Median Absolute Deviation (MAD)3
Skewness-0.029266979
Sum27259
Variance12.063317
MonotonicityNot monotonic
2025-11-12T15:33:30.341203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12300
9.4%
11295
9.2%
3291
9.1%
5277
8.6%
6266
8.3%
13263
8.2%
9261
8.1%
8256
8.0%
14255
8.0%
7250
7.8%
Other values (2)490
15.3%
ValueCountFrequency (%)
3291
9.1%
4246
7.7%
5277
8.6%
6266
8.3%
7250
7.8%
8256
8.0%
9261
8.1%
10244
7.6%
11295
9.2%
12300
9.4%
ValueCountFrequency (%)
14255
8.0%
13263
8.2%
12300
9.4%
11295
9.2%
10244
7.6%
9261
8.1%
8256
8.0%
7250
7.8%
6266
8.3%
5277
8.6%

lead_time_days
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5783396
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.381769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median6
Q38
95-th percentile9
Maximum9
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2924857
Coefficient of variation (CV)0.41096202
Kurtosis-1.2213783
Mean5.5783396
Median Absolute Deviation (MAD)2
Skewness-0.037207287
Sum17873
Variance5.2554907
MonotonicityNot monotonic
2025-11-12T15:33:30.423222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9439
13.7%
5419
13.1%
6416
13.0%
7407
12.7%
3390
12.2%
8386
12.0%
2382
11.9%
4365
11.4%
ValueCountFrequency (%)
2382
11.9%
3390
12.2%
4365
11.4%
5419
13.1%
6416
13.0%
7407
12.7%
8386
12.0%
9439
13.7%
ValueCountFrequency (%)
9439
13.7%
8386
12.0%
7407
12.7%
6416
13.0%
5419
13.1%
4365
11.4%
3390
12.2%
2382
11.9%

daily_demand
Real number (ℝ)

Distinct2375
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.435868
Minimum1.01
Maximum49.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.474408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile3.489
Q113.535
median25.405
Q337.4125
95-th percentile47.49
Maximum49.98
Range48.97
Interquartile range (IQR)23.8775

Descriptive statistics

Standard deviation14.038861
Coefficient of variation (CV)0.55193167
Kurtosis-1.1894344
Mean25.435868
Median Absolute Deviation (MAD)11.96
Skewness-0.0001863122
Sum81496.52
Variance197.08962
MonotonicityNot monotonic
2025-11-12T15:33:30.530345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.725
 
0.2%
12.024
 
0.1%
41.494
 
0.1%
41.014
 
0.1%
20.674
 
0.1%
37.244
 
0.1%
324
 
0.1%
18.154
 
0.1%
34.224
 
0.1%
10.484
 
0.1%
Other values (2365)3163
98.7%
ValueCountFrequency (%)
1.011
 
< 0.1%
1.052
0.1%
1.061
 
< 0.1%
1.071
 
< 0.1%
1.111
 
< 0.1%
1.132
0.1%
1.151
 
< 0.1%
1.161
 
< 0.1%
1.171
 
< 0.1%
1.183
0.1%
ValueCountFrequency (%)
49.982
0.1%
49.972
0.1%
49.961
< 0.1%
49.951
< 0.1%
49.921
< 0.1%
49.91
< 0.1%
49.881
< 0.1%
49.862
0.1%
49.852
0.1%
49.81
< 0.1%

demand_std_dev
Real number (ℝ)

Distinct910
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.260078
Minimum0.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.583725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.97
Q12.9175
median5.235
Q37.5425
95-th percentile9.54
Maximum10
Range9.5
Interquartile range (IQR)4.625

Descriptive statistics

Standard deviation2.7256388
Coefficient of variation (CV)0.5181746
Kurtosis-1.1765609
Mean5.260078
Median Absolute Deviation (MAD)2.315
Skewness0.02919816
Sum16853.29
Variance7.4291071
MonotonicityNot monotonic
2025-11-12T15:33:30.637596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.310
 
0.3%
6.910
 
0.3%
9.4110
 
0.3%
8.978
 
0.2%
0.838
 
0.2%
3.88
 
0.2%
6.718
 
0.2%
7.968
 
0.2%
9.248
 
0.2%
6.688
 
0.2%
Other values (900)3118
97.3%
ValueCountFrequency (%)
0.52
0.1%
0.514
0.1%
0.521
 
< 0.1%
0.533
0.1%
0.561
 
< 0.1%
0.573
0.1%
0.584
0.1%
0.593
0.1%
0.61
 
< 0.1%
0.613
0.1%
ValueCountFrequency (%)
102
 
0.1%
9.984
0.1%
9.973
0.1%
9.964
0.1%
9.954
0.1%
9.943
0.1%
9.931
 
< 0.1%
9.923
0.1%
9.915
0.2%
9.93
0.1%

item_popularity_score
Real number (ℝ)

Distinct91
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54232522
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:30.690099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.14
Q10.31
median0.54
Q30.76
95-th percentile0.95
Maximum1
Range0.9
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.25920011
Coefficient of variation (CV)0.4779422
Kurtosis-1.2042382
Mean0.54232522
Median Absolute Deviation (MAD)0.23
Skewness0.03622376
Sum1737.61
Variance0.067184695
MonotonicityNot monotonic
2025-11-12T15:33:30.743076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4348
 
1.5%
0.446
 
1.4%
0.2245
 
1.4%
0.2845
 
1.4%
0.1845
 
1.4%
0.3444
 
1.4%
0.1344
 
1.4%
0.5444
 
1.4%
0.1443
 
1.3%
0.8343
 
1.3%
Other values (81)2757
86.0%
ValueCountFrequency (%)
0.121
0.7%
0.1129
0.9%
0.1226
0.8%
0.1344
1.4%
0.1443
1.3%
0.1539
1.2%
0.1638
1.2%
0.1733
1.0%
0.1845
1.4%
0.1933
1.0%
ValueCountFrequency (%)
117
0.5%
0.9934
1.1%
0.9832
1.0%
0.9732
1.0%
0.9638
1.2%
0.9540
1.2%
0.9423
0.7%
0.9332
1.0%
0.9234
1.1%
0.9139
1.2%
Distinct100
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size187.6 KiB
2025-11-12T15:33:30.847031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9282147
Min length2

Characters and Unicode

Total characters9382
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL82
2nd rowL15
3rd rowL4
4th rowL95
5th rowL36
ValueCountFrequency (%)
l5741
 
1.3%
l3141
 
1.3%
l5941
 
1.3%
l8740
 
1.2%
l3740
 
1.2%
l8340
 
1.2%
l7139
 
1.2%
l3939
 
1.2%
l8539
 
1.2%
l9939
 
1.2%
Other values (90)2805
87.5%
2025-11-12T15:33:31.004090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L3204
34.2%
1683
 
7.3%
8683
 
7.3%
9675
 
7.2%
7660
 
7.0%
5658
 
7.0%
3643
 
6.9%
4628
 
6.7%
6611
 
6.5%
2586
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)9382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L3204
34.2%
1683
 
7.3%
8683
 
7.3%
9675
 
7.2%
7660
 
7.0%
5658
 
7.0%
3643
 
6.9%
4628
 
6.7%
6611
 
6.5%
2586
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L3204
34.2%
1683
 
7.3%
8683
 
7.3%
9675
 
7.2%
7660
 
7.0%
5658
 
7.0%
3643
 
6.9%
4628
 
6.7%
6611
 
6.5%
2586
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L3204
34.2%
1683
 
7.3%
8683
 
7.3%
9675
 
7.2%
7660
 
7.0%
5658
 
7.0%
3643
 
6.9%
4628
 
6.7%
6611
 
6.5%
2586
 
6.2%

zone
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size181.6 KiB
D
833 
A
812 
B
784 
C
775 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3204
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
D833
26.0%
A812
25.3%
B784
24.5%
C775
24.2%

Length

2025-11-12T15:33:31.053383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-12T15:33:31.092666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
d833
26.0%
a812
25.3%
b784
24.5%
c775
24.2%

Most occurring characters

ValueCountFrequency (%)
D833
26.0%
A812
25.3%
B784
24.5%
C775
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D833
26.0%
A812
25.3%
B784
24.5%
C775
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D833
26.0%
A812
25.3%
B784
24.5%
C775
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D833
26.0%
A812
25.3%
B784
24.5%
C775
24.2%

picking_time_seconds
Real number (ℝ)

Distinct170
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.606429
Minimum10
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.138729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile19
Q153
median96
Q3138
95-th percentile172
Maximum179
Range169
Interquartile range (IQR)85

Descriptive statistics

Standard deviation49.218084
Coefficient of variation (CV)0.5147989
Kurtosis-1.20733
Mean95.606429
Median Absolute Deviation (MAD)43
Skewness-0.025226907
Sum306323
Variance2422.4198
MonotonicityNot monotonic
2025-11-12T15:33:31.374419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11534
 
1.1%
15231
 
1.0%
17730
 
0.9%
4630
 
0.9%
5330
 
0.9%
2228
 
0.9%
9628
 
0.9%
12827
 
0.8%
17227
 
0.8%
10827
 
0.8%
Other values (160)2912
90.9%
ValueCountFrequency (%)
1019
0.6%
1114
0.4%
1220
0.6%
1316
0.5%
1426
0.8%
1515
0.5%
1614
0.4%
1711
0.3%
1817
0.5%
1915
0.5%
ValueCountFrequency (%)
17918
0.6%
17822
0.7%
17730
0.9%
17620
0.6%
17518
0.6%
17423
0.7%
17319
0.6%
17227
0.8%
17116
0.5%
17017
0.5%

handling_cost_per_unit
Real number (ℝ)

Distinct451
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7771161
Minimum0.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.431245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.71
Q11.66
median2.81
Q33.91
95-th percentile4.77
Maximum5
Range4.5
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.3052132
Coefficient of variation (CV)0.4699887
Kurtosis-1.2197597
Mean2.7771161
Median Absolute Deviation (MAD)1.11
Skewness-0.042389862
Sum8897.88
Variance1.7035814
MonotonicityNot monotonic
2025-11-12T15:33:31.485453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9116
 
0.5%
1.9716
 
0.5%
4.8414
 
0.4%
3.6614
 
0.4%
1.5314
 
0.4%
4.9114
 
0.4%
4.8113
 
0.4%
3.7913
 
0.4%
1.2413
 
0.4%
1.8913
 
0.4%
Other values (441)3064
95.6%
ValueCountFrequency (%)
0.56
0.2%
0.516
0.2%
0.526
0.2%
0.536
0.2%
0.5411
0.3%
0.557
0.2%
0.565
0.2%
0.574
 
0.1%
0.583
 
0.1%
0.5912
0.4%
ValueCountFrequency (%)
55
 
0.2%
4.996
0.2%
4.983
 
0.1%
4.979
0.3%
4.961
 
< 0.1%
4.956
0.2%
4.943
 
0.1%
4.9310
0.3%
4.924
 
0.1%
4.9114
0.4%

unit_price
Real number (ℝ)

Distinct2962
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.88757
Minimum10.22
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.537739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10.22
5-th percentile20.349
Q159.76
median106.005
Q3152.41
95-th percentile189.9825
Maximum200
Range189.78
Interquartile range (IQR)92.65

Descriptive statistics

Standard deviation54.428161
Coefficient of variation (CV)0.5140184
Kurtosis-1.1815929
Mean105.88757
Median Absolute Deviation (MAD)46.335
Skewness-0.02397259
Sum339263.79
Variance2962.4248
MonotonicityNot monotonic
2025-11-12T15:33:31.594660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.664
 
0.1%
159.773
 
0.1%
130.213
 
0.1%
111.073
 
0.1%
161.783
 
0.1%
11.173
 
0.1%
147.623
 
0.1%
68.933
 
0.1%
148.193
 
0.1%
72.833
 
0.1%
Other values (2952)3173
99.0%
ValueCountFrequency (%)
10.221
 
< 0.1%
10.31
 
< 0.1%
10.361
 
< 0.1%
10.381
 
< 0.1%
10.581
 
< 0.1%
10.721
 
< 0.1%
10.823
0.1%
10.871
 
< 0.1%
10.911
 
< 0.1%
10.952
0.1%
ValueCountFrequency (%)
2001
< 0.1%
199.971
< 0.1%
199.921
< 0.1%
199.91
< 0.1%
199.891
< 0.1%
199.861
< 0.1%
199.831
< 0.1%
199.61
< 0.1%
199.511
< 0.1%
199.461
< 0.1%

holding_cost_per_unit_day
Real number (ℝ)

Distinct191
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0459925
Minimum0.1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.650737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.19
Q10.58
median1.05
Q31.5225
95-th percentile1.9
Maximum2
Range1.9
Interquartile range (IQR)0.9425

Descriptive statistics

Standard deviation0.55047969
Coefficient of variation (CV)0.52627499
Kurtosis-1.2038743
Mean1.0459925
Median Absolute Deviation (MAD)0.47
Skewness-0.0071353588
Sum3351.36
Variance0.30302789
MonotonicityNot monotonic
2025-11-12T15:33:31.708178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3428
 
0.9%
1.7128
 
0.9%
0.6925
 
0.8%
1.2724
 
0.7%
0.7624
 
0.7%
1.8324
 
0.7%
1.5624
 
0.7%
1.6223
 
0.7%
1.7522
 
0.7%
0.322
 
0.7%
Other values (181)2960
92.4%
ValueCountFrequency (%)
0.111
0.3%
0.1115
0.5%
0.1220
0.6%
0.1320
0.6%
0.1416
0.5%
0.1519
0.6%
0.1620
0.6%
0.1719
0.6%
0.1818
0.6%
0.1918
0.6%
ValueCountFrequency (%)
213
0.4%
1.9911
0.3%
1.9816
0.5%
1.9715
0.5%
1.9618
0.6%
1.9512
0.4%
1.9419
0.6%
1.9321
0.7%
1.9215
0.5%
1.9117
0.5%

stockout_count_last_month
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.602372
Minimum0
Maximum9
Zeros294
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.755976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8837083
Coefficient of variation (CV)0.62657
Kurtosis-1.2356556
Mean4.602372
Median Absolute Deviation (MAD)3
Skewness-0.02542448
Sum14746
Variance8.3157733
MonotonicityNot monotonic
2025-11-12T15:33:31.797227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9358
11.2%
6330
10.3%
2324
10.1%
7323
10.1%
8323
10.1%
3321
10.0%
1314
9.8%
4311
9.7%
5306
9.6%
0294
9.2%
ValueCountFrequency (%)
0294
9.2%
1314
9.8%
2324
10.1%
3321
10.0%
4311
9.7%
5306
9.6%
6330
10.3%
7323
10.1%
8323
10.1%
9358
11.2%
ValueCountFrequency (%)
9358
11.2%
8323
10.1%
7323
10.1%
6330
10.3%
5306
9.6%
4311
9.7%
3321
10.0%
2324
10.1%
1314
9.8%
0294
9.2%

order_fulfillment_rate
Real number (ℝ)

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84973783
Minimum0.7
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.841190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.71
Q10.78
median0.85
Q30.92
95-th percentile0.98
Maximum1
Range0.3
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.086703207
Coefficient of variation (CV)0.10203524
Kurtosis-1.1860735
Mean0.84973783
Median Absolute Deviation (MAD)0.07
Skewness0.0051372055
Sum2722.56
Variance0.0075174461
MonotonicityNot monotonic
2025-11-12T15:33:31.891589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.92121
 
3.8%
0.85120
 
3.7%
0.88120
 
3.7%
0.73118
 
3.7%
0.79118
 
3.7%
0.77118
 
3.7%
0.87113
 
3.5%
0.98112
 
3.5%
0.78111
 
3.5%
0.8111
 
3.5%
Other values (21)2042
63.7%
ValueCountFrequency (%)
0.758
1.8%
0.71107
3.3%
0.72100
3.1%
0.73118
3.7%
0.74101
3.2%
0.75106
3.3%
0.7687
2.7%
0.77118
3.7%
0.78111
3.5%
0.79118
3.7%
ValueCountFrequency (%)
155
1.7%
0.99103
3.2%
0.98112
3.5%
0.97108
3.4%
0.96104
3.2%
0.95103
3.2%
0.94105
3.3%
0.93107
3.3%
0.92121
3.8%
0.9179
2.5%

total_orders_last_month
Real number (ℝ)

Distinct922
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.71473
Minimum50
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:31.946066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile96
Q1283
median513.5
Q3764.25
95-th percentile955
Maximum999
Range949
Interquartile range (IQR)481.25

Descriptive statistics

Standard deviation276.76853
Coefficient of variation (CV)0.53049783
Kurtosis-1.2184761
Mean521.71473
Median Absolute Deviation (MAD)237.5
Skewness0.035935044
Sum1671574
Variance76600.82
MonotonicityNot monotonic
2025-11-12T15:33:32.000229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81210
 
0.3%
2669
 
0.3%
9389
 
0.3%
7749
 
0.3%
909
 
0.3%
2769
 
0.3%
969
 
0.3%
1288
 
0.2%
6958
 
0.2%
8388
 
0.2%
Other values (912)3116
97.3%
ValueCountFrequency (%)
504
0.1%
515
0.2%
526
0.2%
531
 
< 0.1%
543
0.1%
555
0.2%
566
0.2%
572
 
0.1%
585
0.2%
591
 
< 0.1%
ValueCountFrequency (%)
9994
0.1%
9983
0.1%
9974
0.1%
9965
0.2%
9956
0.2%
9945
0.2%
9934
0.1%
9924
0.1%
9914
0.1%
9903
0.1%

turnover_ratio
Real number (ℝ)

Distinct1250
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1236767
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:32.050866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.77
Q14.59
median8.15
Q311.6925
95-th percentile14.38
Maximum15
Range14
Interquartile range (IQR)7.1025

Descriptive statistics

Standard deviation4.0691912
Coefficient of variation (CV)0.50090511
Kurtosis-1.2171964
Mean8.1236767
Median Absolute Deviation (MAD)3.55
Skewness-0.0174314
Sum26028.26
Variance16.558317
MonotonicityNot monotonic
2025-11-12T15:33:32.105049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.99
 
0.3%
7.339
 
0.3%
11.88
 
0.2%
6.428
 
0.2%
13.678
 
0.2%
3.278
 
0.2%
12.078
 
0.2%
7.977
 
0.2%
2.867
 
0.2%
5.977
 
0.2%
Other values (1240)3125
97.5%
ValueCountFrequency (%)
12
0.1%
1.011
 
< 0.1%
1.023
0.1%
1.033
0.1%
1.051
 
< 0.1%
1.062
0.1%
1.071
 
< 0.1%
1.082
0.1%
1.091
 
< 0.1%
1.11
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
14.991
 
< 0.1%
14.985
0.2%
14.971
 
< 0.1%
14.964
0.1%
14.952
 
0.1%
14.943
0.1%
14.933
0.1%
14.923
0.1%
14.912
 
0.1%

layout_efficiency_score
Real number (ℝ)

Distinct81
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60058052
Minimum0.2
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:32.164593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.24
Q10.4
median0.6
Q30.8
95-th percentile0.96
Maximum1
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.23051079
Coefficient of variation (CV)0.38381329
Kurtosis-1.1911169
Mean0.60058052
Median Absolute Deviation (MAD)0.2
Skewness-0.012368995
Sum1924.26
Variance0.053135223
MonotonicityNot monotonic
2025-11-12T15:33:32.228483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8354
 
1.7%
0.6553
 
1.7%
0.2751
 
1.6%
0.2649
 
1.5%
0.7549
 
1.5%
0.5749
 
1.5%
0.4149
 
1.5%
0.4648
 
1.5%
0.6146
 
1.4%
0.7946
 
1.4%
Other values (71)2710
84.6%
ValueCountFrequency (%)
0.219
 
0.6%
0.2141
1.3%
0.2244
1.4%
0.2324
0.7%
0.2440
1.2%
0.2542
1.3%
0.2649
1.5%
0.2751
1.6%
0.2840
1.2%
0.2944
1.4%
ValueCountFrequency (%)
121
0.7%
0.9944
1.4%
0.9836
1.1%
0.9739
1.2%
0.9639
1.2%
0.9538
1.2%
0.9444
1.4%
0.9337
1.2%
0.9236
1.1%
0.9131
1.0%
Distinct364
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size25.2 KiB
Minimum2024-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-12T15:33:32.291537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:32.350059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

forecasted_demand_next_7d
Real number (ℝ)

Distinct3009
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.28625
Minimum10.09
Maximum299.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:32.406519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10.09
5-th percentile24.9615
Q183.19
median152.87
Q3225.44
95-th percentile284.2115
Maximum299.94
Range289.85
Interquartile range (IQR)142.25

Descriptive statistics

Standard deviation82.945255
Coefficient of variation (CV)0.53760628
Kurtosis-1.1864876
Mean154.28625
Median Absolute Deviation (MAD)71.15
Skewness0.01121432
Sum494333.13
Variance6879.9153
MonotonicityNot monotonic
2025-11-12T15:33:32.461080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.353
 
0.1%
79.883
 
0.1%
97.223
 
0.1%
131.23
 
0.1%
292.143
 
0.1%
19.973
 
0.1%
170.063
 
0.1%
137.393
 
0.1%
96.582
 
0.1%
291.392
 
0.1%
Other values (2999)3176
99.1%
ValueCountFrequency (%)
10.091
< 0.1%
10.151
< 0.1%
10.212
0.1%
10.291
< 0.1%
10.331
< 0.1%
10.781
< 0.1%
10.81
< 0.1%
10.931
< 0.1%
10.981
< 0.1%
11.021
< 0.1%
ValueCountFrequency (%)
299.941
< 0.1%
299.861
< 0.1%
299.731
< 0.1%
299.681
< 0.1%
299.661
< 0.1%
299.581
< 0.1%
299.511
< 0.1%
299.191
< 0.1%
299.071
< 0.1%
299.021
< 0.1%

KPI_score
Real number (ℝ)

High correlation 

Distinct519
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60173658
Minimum0.259
Maximum0.936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.2 KiB
2025-11-12T15:33:32.512968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.259
5-th percentile0.425
Q10.527
median0.601
Q30.67725
95-th percentile0.77985
Maximum0.936
Range0.677
Interquartile range (IQR)0.15025

Descriptive statistics

Standard deviation0.10828597
Coefficient of variation (CV)0.17995577
Kurtosis-0.24668936
Mean0.60173658
Median Absolute Deviation (MAD)0.075
Skewness-0.018617004
Sum1927.964
Variance0.011725851
MonotonicityNot monotonic
2025-11-12T15:33:32.568512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59319
 
0.6%
0.66819
 
0.6%
0.64418
 
0.6%
0.57717
 
0.5%
0.60117
 
0.5%
0.60317
 
0.5%
0.60517
 
0.5%
0.6416
 
0.5%
0.58116
 
0.5%
0.53516
 
0.5%
Other values (509)3032
94.6%
ValueCountFrequency (%)
0.2591
 
< 0.1%
0.2651
 
< 0.1%
0.2731
 
< 0.1%
0.2791
 
< 0.1%
0.3021
 
< 0.1%
0.3071
 
< 0.1%
0.3081
 
< 0.1%
0.3181
 
< 0.1%
0.3191
 
< 0.1%
0.323
0.1%
ValueCountFrequency (%)
0.9361
< 0.1%
0.9241
< 0.1%
0.8952
0.1%
0.8921
< 0.1%
0.8851
< 0.1%
0.8831
< 0.1%
0.8781
< 0.1%
0.8742
0.1%
0.8711
< 0.1%
0.8691
< 0.1%

Interactions

2025-11-12T15:33:28.692548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.702510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.470315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.220897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.934729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.743156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.455558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.234170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.942190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.684662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.490206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.236862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.000174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.831876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.577489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.298522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.030662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.950826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.731882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.743711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.508820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.260130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.974833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.781755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.492790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.273708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.981124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.723058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.529774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.277445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.038216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.872518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.616153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.337915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.072541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.989779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.771681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.780599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.543762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.296847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.012977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.819222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.528911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.310321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.019329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.856313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.567726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.317793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.075023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.911083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.653918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.376615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.116991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.029960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.811476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.818655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.581885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.335381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.053643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.858543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.566373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.348557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.059157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.896386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.609233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.359239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.113206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.951821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.693277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.416269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.158705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.069985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.853455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.910617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.622197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.378787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.096209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.899559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.608105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.390063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.102621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.936926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.651899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.405465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.153138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.995265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.733260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.458404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.203309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.112618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.893539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.948781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.659601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.417180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.135632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.938999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.645919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.429777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.142055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.977833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.692544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.446962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.192945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.035718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.772340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.498224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.393103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.153347image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.932287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:15.987068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.696192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.454699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.237144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.976296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.682792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.465999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.179770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.013829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.731623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.487501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.346788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.073545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.808472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.534895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.433332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.191534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.969611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.024271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.734096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.492577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.277873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.013373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.718593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.502485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.217646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.050860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.772241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.527603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.386724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.112786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.845649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.572346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.472885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.229449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.011612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.064371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.773054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.532434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.320635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.054979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.836027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.543243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.259112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.090949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.814547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.571416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.434129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.155823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.888170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.614166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.518102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.272020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.049203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.101164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.809249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.569621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.360381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.091143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.874325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.579240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.297401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.128072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.853853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.610288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.471517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.194327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.937470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.652181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.557816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.309721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.091061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.145594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.850572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.611439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.403251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.133276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.916327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.620945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.339694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.168898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.895968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.654433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.512506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.236944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.978220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.693716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.601591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.352995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.136475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.189186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.945528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.654692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.449018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.176338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.958256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.663061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.384338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.210741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.942072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.698668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.554671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.282318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.022299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.738731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.650038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.397819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.174440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.226795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.983535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.691482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.488615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.213374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.996490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.700839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.423361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.247302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.982128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.739359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.590465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.320157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.058275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.777406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.689781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.438937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.215658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.267409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.023693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.733220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.531092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.254260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.035961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.742075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.465504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.286679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.023997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.782152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.631688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.364368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.098805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.818400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.733120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.480378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.255266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.305351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.059287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.769972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.570380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.292205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.073260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.779749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.505602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.324280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.064389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.823882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.669108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.405695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.136065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.856166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.774761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.520410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.296086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.347249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.099438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.809431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.612289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.330967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.112231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.818276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.546693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.364619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.105292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.866722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.707624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.447326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.174820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.896253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.817246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.562811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.339161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.390394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.142106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.854323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.657810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.375550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.154637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.862115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.591196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.408327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.151799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.913745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.751980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.492626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.217876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.940294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.863230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.609224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:29.383947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:16.429977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.181421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:17.894754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:18.700883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:19.415799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.194891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:20.902915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:21.643487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:22.450604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.194790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:23.957306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:24.793005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:25.535403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.258741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:26.982239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:27.908381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-12T15:33:28.651198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-11-12T15:33:32.620110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
KPI_scorecategorydaily_demanddemand_std_devforecasted_demand_next_7dhandling_cost_per_unitholding_cost_per_unit_dayitem_popularity_scorelayout_efficiency_scorelead_time_daysorder_fulfillment_ratepicking_time_secondsreorder_frequency_daysreorder_pointstock_levelstockout_count_last_monthtotal_orders_last_monthturnover_ratiounit_pricezone
KPI_score1.0000.000-0.0050.0320.0150.003-0.496-0.0300.4180.0240.1390.0170.007-0.0280.011-0.533-0.0110.491-0.0070.016
category0.0001.0000.0000.0000.0030.0130.0330.0150.0000.0000.0000.0000.0000.0170.0000.0000.0200.0000.0000.030
daily_demand-0.0050.0001.000-0.005-0.0270.0040.0150.0030.013-0.004-0.0190.0010.026-0.014-0.0190.0020.0070.000-0.0150.021
demand_std_dev0.0320.000-0.0051.000-0.014-0.024-0.0210.0010.000-0.0090.0060.0330.0180.017-0.012-0.026-0.0310.0050.0140.000
forecasted_demand_next_7d0.0150.003-0.027-0.0141.0000.0020.020-0.016-0.0000.0000.013-0.0170.033-0.0120.012-0.0220.0150.018-0.0120.002
handling_cost_per_unit0.0030.0130.004-0.0240.0021.000-0.006-0.036-0.0170.0120.0050.008-0.0180.0040.0370.012-0.0040.015-0.0060.000
holding_cost_per_unit_day-0.4960.0330.015-0.0210.020-0.0061.0000.0280.005-0.041-0.008-0.0170.0130.002-0.006-0.0140.007-0.006-0.0070.000
item_popularity_score-0.0300.0150.0030.001-0.016-0.0360.0281.000-0.009-0.019-0.0030.0080.0040.0250.004-0.000-0.013-0.021-0.0040.000
layout_efficiency_score0.4180.0000.0130.000-0.000-0.0170.005-0.0091.0000.010-0.0240.0040.022-0.009-0.002-0.033-0.014-0.0130.0070.000
lead_time_days0.0240.000-0.004-0.0090.0000.012-0.041-0.0190.0101.0000.008-0.0100.015-0.004-0.006-0.0150.035-0.017-0.0230.000
order_fulfillment_rate0.1390.000-0.0190.0060.0130.005-0.008-0.003-0.0240.0081.0000.0240.0120.0230.0090.0020.016-0.011-0.0070.000
picking_time_seconds0.0170.0000.0010.033-0.0170.008-0.0170.0080.004-0.0100.0241.000-0.008-0.016-0.0300.008-0.0090.008-0.0290.000
reorder_frequency_days0.0070.0000.0260.0180.033-0.0180.0130.0040.0220.0150.012-0.0081.000-0.001-0.025-0.002-0.021-0.004-0.0040.010
reorder_point-0.0280.017-0.0140.017-0.0120.0040.0020.025-0.009-0.0040.023-0.016-0.0011.000-0.0380.029-0.006-0.0230.0080.033
stock_level0.0110.000-0.019-0.0120.0120.037-0.0060.004-0.002-0.0060.009-0.030-0.025-0.0381.0000.0010.0120.0120.0080.000
stockout_count_last_month-0.5330.0000.002-0.026-0.0220.012-0.014-0.000-0.033-0.0150.0020.008-0.0020.0290.0011.0000.011-0.0030.0210.027
total_orders_last_month-0.0110.0200.007-0.0310.015-0.0040.007-0.013-0.0140.0350.016-0.009-0.021-0.0060.0120.0111.0000.009-0.0200.020
turnover_ratio0.4910.0000.0000.0050.0180.015-0.006-0.021-0.013-0.017-0.0110.008-0.004-0.0230.012-0.0030.0091.0000.0060.000
unit_price-0.0070.000-0.0150.014-0.012-0.006-0.007-0.0040.007-0.023-0.007-0.029-0.0040.0080.0080.021-0.0200.0061.0000.035
zone0.0160.0300.0210.0000.0020.0000.0000.0000.0000.0000.0000.0000.0100.0330.0000.0270.0200.0000.0351.000

Missing values

2025-11-12T15:33:29.455639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-12T15:33:29.587739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

item_idcategorystock_levelreorder_pointreorder_frequency_dayslead_time_daysdaily_demanddemand_std_devitem_popularity_scorestorage_location_idzonepicking_time_secondshandling_cost_per_unitunit_priceholding_cost_per_unit_daystockout_count_last_monthorder_fulfillment_ratetotal_orders_last_monthturnover_ratiolayout_efficiency_scorelast_restock_dateforecasted_demand_next_7dKPI_score
0ITM10000Pharma283214449.851.560.43L82B1063.61117.801.1400.807003.330.332024-02-17184.370.556
1ITM10001Automotive301529623.342.550.69L15A453.54178.801.0930.7973610.360.982024-10-01221.940.723
2ITM10002Groceries1326011837.693.150.62L4B1770.5254.050.9570.7581414.320.872024-04-0753.850.680
3ITM10003Automotive3464613533.692.790.21L95A392.6431.101.9000.969942.080.292024-01-2792.040.488
4ITM10004Automotive49554649.585.230.31L36D351.40104.970.6350.832995.650.962024-05-17194.580.670
5ITM10005Apparel154629635.959.470.41L32A941.96164.120.6610.994777.610.952024-07-12180.650.803
6ITM10006Groceries8697649.044.720.28L29C1104.90134.380.3780.884184.250.452024-12-10223.690.526
7ITM10007Groceries848913447.826.780.24L18D1300.88131.091.4270.7495811.370.702024-11-05195.750.558
8ITM10008Groceries311224933.727.040.15L95D403.7513.320.7180.8669710.640.292024-11-04105.740.541
9ITM10009Automotive4498983.918.780.89L14A261.2870.321.6381.004962.650.272024-01-21154.780.366
item_idcategorystock_levelreorder_pointreorder_frequency_dayslead_time_daysdaily_demanddemand_std_devitem_popularity_scorestorage_location_idzonepicking_time_secondshandling_cost_per_unitunit_priceholding_cost_per_unit_daystockout_count_last_monthorder_fulfillment_ratetotal_orders_last_monthturnover_ratiolayout_efficiency_scorelast_restock_dateforecasted_demand_next_7dKPI_score
3194ITM13194Apparel395329425.682.130.55L18B1791.17122.040.3320.796819.020.352024-12-1393.860.675
3195ITM13195Automotive282924510.328.380.93L50D1004.8027.131.8610.9428613.200.842024-11-06203.980.726
3196ITM13196Pharma286485243.945.740.32L32B1463.2663.600.5080.7679714.820.302024-04-1169.610.600
3197ITM13197Apparel378979329.049.850.84L33B1323.78171.440.2650.878669.180.742024-10-2230.090.718
3198ITM13198Electronics336899610.417.330.38L27C134.5512.101.1370.764923.180.642024-10-21177.980.469
3199ITM13199Groceries3432112239.881.300.34L43C994.3424.631.5030.798711.800.202024-11-28237.040.545
3200ITM13200Electronics42843572.684.250.91L83B1051.4879.041.4640.7783314.960.392024-11-2834.000.605
3201ITM13201Groceries4158014549.155.410.14L11D380.76199.891.1190.899377.630.602024-10-0262.570.509
3202ITM13202Groceries173843943.398.470.69L58B954.6665.451.0440.869056.370.462024-03-3036.960.565
3203ITM13203Apparel3774014329.407.430.14L48A564.9239.881.9490.928128.740.542024-05-25193.910.435